About this Research Topic
Nearly 18 million people die each year from cardiac events, often suddenly. Among the various unmet needs, coronary artery phenotyping represents the main challenge to be undertaken to significantly reduce the routine diagnostic processes and improve therapeutic and interventional choices. The integrative-omics approach together with cognitive computing applied to 3D-vessel reconstruction have revolutionized the cardio-biomedicine research. Artificial Intelligence (AI)-assisted diagnostic imaging and complex biological data might ultimately be an effective screening combo for a deep coronary artery analysis guiding toward preventive approaches, tailored therapies, non-invasive patient examinations and advanced cares.
With this Research Topic we would like to collect the recent experimental advances and point of views in mechanisms behind biological phenotyping of coronary arteries analyzed by Artificial Intelligence algorithms. All the contributors are invited to submit articles concerning the use of Artificial Intelligence applied to in vivo, ex vivo, in vitro, and in silico models of coronary artery diseases.
With this Research Topic we would like to collect the recent experimental advances and point of views in mechanisms behind biological phenotyping of coronary arteries analyzed by Artificial Intelligence algorithms. All the contributors are invited to submit articles concerning the use of Artificial Intelligence applied to in vivo, ex vivo, in vitro, and in silico models of coronary artery diseases.
Keywords: Plaque phenotyping, atherosclerosis, acute coronary syndrome, shear stress, plaque erosion, plaque rupture, obstructive and no-obstructive coronary artery diseases, immune system, thromboinflammation, personalized medicine
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
